The paper presents efficient algorithms for finding shortest paths in sparse networks, which are faster than previously known algorithms. The authors survey and analyze known results, introduce a new implementation for priority queues, and propose a class of "arc set partition" algorithms. For the single source problem on networks with nonnegative arcs, the running time is $O(\min(n^{1+1/k} + \epsilon, (n + \epsilon) \log n))$, where $n$ is the number of nodes, $e$ is the number of arcs, and $k$ is a fixed integer. For the single source and all pairs problem on unrestricted networks, the running time is $O(\min(n^{2+1/k} + ne, n^2 \log n + ne \log n))$. The paper also discusses the behavior of Algorithm A on unrestricted networks and introduces an algorithm for the all pairs problem with the same complexity as the single source algorithm. The algorithms are designed to be both theoretically sound and practical, making them useful for solving shortest path problems in sparse networks.The paper presents efficient algorithms for finding shortest paths in sparse networks, which are faster than previously known algorithms. The authors survey and analyze known results, introduce a new implementation for priority queues, and propose a class of "arc set partition" algorithms. For the single source problem on networks with nonnegative arcs, the running time is $O(\min(n^{1+1/k} + \epsilon, (n + \epsilon) \log n))$, where $n$ is the number of nodes, $e$ is the number of arcs, and $k$ is a fixed integer. For the single source and all pairs problem on unrestricted networks, the running time is $O(\min(n^{2+1/k} + ne, n^2 \log n + ne \log n))$. The paper also discusses the behavior of Algorithm A on unrestricted networks and introduces an algorithm for the all pairs problem with the same complexity as the single source algorithm. The algorithms are designed to be both theoretically sound and practical, making them useful for solving shortest path problems in sparse networks.